High-profile companies, like the ecommerce ones, have laid off employees. Ola fired 1000 employees. India’s e-commerce marketplace Flipkart laid off 800. Other startups have been cutting jobs too. Not only that, things have been changing at the global level too. Cisco is cutting 14,000 jobs in 2017 and Intel is laying off 12,000. Startups like Zomato and Foodpanda fired people. Reasons for the firings could be myriad: from overrecruitment to poor financials to bad management decisions. Therefore, companies need workforce optimization. Lay offs themselves have several disadvantages: resources can migrate; frustration in retained employees; long-term damage to company’s image and reputation. Consequently, workforce optimization is the need of the hour.
Big Data and Analytics can help HR recruitment and workforce optimization
Big Data Analytics tools help in avoiding layoffs and conducting sensible hiring. Well-organized recruitment is a must to prevent mass firings. These technologies can help companies deal with the huge number of candidates to screen. Additionally, they factor in several aspects such as market conditions, background checks, manpower requirement, etc. Additionally, salary processing and appraisals can be tedious, more so in big companies. The data can be difficult to handle. Bringing Big Data into the picture would remove chances of human error.
Unraveling Big Data and Analytics
Big Data is a term for datasets so complex that traditional data-processing methods will be insufficient. Big Data Analytics tools process datasets to find unknown correlations and hidden patterns, thus leading to the discovery of meaningful and significant candidate preferences, market trends, and other important recruitment information. Analytics uses data that may be untapped through conventional programs, such as social media, the Internet, survey results, and candidate emails. HR firms can use Analytics to predict, describe, and improve recruitment, leading to better workforce optimization. The tools can conduct sentiment analysis too over social media data collated from platforms such as LinkedIn, Facebook, Twitter, etc. A company could use Analytics to find market sentiment and decide when to go for recruitment.
Big Data Analytics can leverage statistical tools and algorithms
Big Data Analytics uses statistical tools and algorithms to find out trends and do predictions. It factors in parameters including compensation and benefits, level of education, and job history. All this leads to workforce optimization, performance assessment, and maintenance and prediction of optimal labor requirements, helping recruiting managers decide who to hire and how to hire.
“It is not practical to expect managers would always take foolproof decisions. They can go wrong. In fact, errors increase as the number of parameters increases. People can’t track such huge data. So, it’s best to leave it to algorithms,” said Somesh Misra, VP, Products and HR, Deskera—a leading global provider of business software having its Big Data Analytics tool.
Several such tools like Workforce Analytics and AppDynamics can help HR departments reduce their burden. They not only predict and assess a potential candidate, it also tracks significant feed including social media. For example, extrapolating information on frequency of a candidate’s visits to sites like LinkedIn and page updates, finding out whether candidate is exploring other options, or asking for recommendations from LinkedIn users. The tools provide information on aspects such as cultural fit, a candidate’s personality with regard to organization values, etc.
Big Data Analytics can give organizations the edge
The tools help organizations avoid firing over the long term and help retain resources crucial to growth. In the corporate world, companies must be good at identifying, recruiting, and retaining talent. Through Big Data Analytics, HR departments can generate, collect, visualize and access data in new ways. These technologies can ensure that the reputation and image of an organization doesn’t get spoiled.